This workflow finds disease relevant to the query string via the following steps: 1. A user query: a list of terms or boolean query - look at the Apache Lucene project for all details. E.g.: (EZH2 OR "Enhancer of Zeste" +(mutation chromatin) -clinical); consider adding 'ProteinSynonymsToQuery' in front of the input if your query is a protein. 2. Retrieve documents: finds 'maximumNumberOfHits' relevant documents (abstract+title) based on query (the AIDA service inside is based on Apa...

This workflow finds proteins relevant to the query string via the following steps:
A user query: a single gene/protein name. E.g.: (EZH2 OR "Enhancer of Zeste").
Retrieve documents: finds 'maximumNumberOfHits' relevant documents (abstract+title) based on query (the AIDA service inside is based on Apache's Lucene)
Discover proteins: extract proteins discovered in the set of relevant abstracts with a 'named entity recognizer' trained on genomic terms using a Bayesian approach; the AIDA serv...

This workflow was based on BioAID_DiseaseDiscovery, changes: expects only one protein name, adds protein synonyms).
This workflow finds diseases relevant to the query string via the following steps:
A user query: a single protein name
Add synonyms (service courtesy of Martijn Scheumie, Erasmus University Rotterdam)
Retrieve documents: finds relevant documents (abstract+title) based on query
Discover proteins: extract proteins discovered in the set of relevant abstracts
5. Link proteins ...

This workflow discovers proteins from plain text. It is built around the AIDA 'Named Entity Recognize' web service by Sophia Katrenko (service based on LingPipe), from which output it filters out proteins. The Named Recognizer services uses the pre-learned genomics model, named 'MedLine', to find genomics concepts in plain text.